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  1. Norms of inquiry.David Thorstad - forthcoming - Philosophical Topics.
    Epistemologists have recently proposed a number of norms governing rational inquiry. My aim in this paper is to unify and explain recently proposed norms of inquiry by developing a general account of the conditions under which inquiries are rational, analogous to theories such as evidentialism and reliabilism for rational belief. I begin with a reason-responsiveness conception of rationality as responding correctly to possessed normative reasons. I extend this account with a series of claims about the normative reasons for inquiry that (...)
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  • A minimax algorithm better than alpha-beta? Yes and No.Igor Roizen & Judea Pearl - 1983 - Artificial Intelligence 21 (1-2):199-220.
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  • A comparison of minimax tree search algorithms.Murray S. Campbell & T. A. Marsland - 1983 - Artificial Intelligence 20 (4):347-367.
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  • Low overhead alternatives to SSS∗.T. A. Marsland, Alexander Reinefeld & Jonathan Schaeffer - 1987 - Artificial Intelligence 31 (2):185-199.
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  • Pruning algorithms for multi-model adversary search.David Carmel & Shaul Markovitch - 1998 - Artificial Intelligence 99 (2):325-355.
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  • Best-first fixed-depth minimax algorithms.Aske Plaat, Jonathan Schaeffer, Wim Pijls & Arie de Bruin - 1996 - Artificial Intelligence 87 (1-2):255-293.
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  • The Accidental Philosopher and One of the Hardest Problems in the World.Sonje Finnestad & Eric Neufeld - 2022 - Philosophies 7 (4):76.
    Given the difficulties of defining “machine” and “think”, Turing proposed to replace the question “Can machines think?” with a proxy: how well can an agent engage in sustained conversation with a human? Though Turing neither described himself as a philosopher nor published much on philosophical matters, his Imitation Game has stood the test of time. Most understood at that time that success would not come easy, but few would have guessed just how difficult engaging in ordinary conversation would turn out (...)
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  • The *-minimax search procedure for trees containing chance nodes.Bruce W. Ballard - 1983 - Artificial Intelligence 21 (3):327-350.
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  • On the branching factor of the alpha-beta pruning algorithm.Gérard M. Baudet - 1978 - Artificial Intelligence 10 (2):173-199.
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  • Asymptotic properties of minimax trees and game-searching procedures.Judea Pearl - 1980 - Artificial Intelligence 14 (2):113-138.
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  • Parallelism in alpha-beta search.Raphael A. Finkel & John P. Fishburn - 1982 - Artificial Intelligence 19 (1):89-106.
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  • An investigation of the causes of pathology in games.Dana S. Nau - 1982 - Artificial Intelligence 19 (3):257-278.
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  • Generalization of alpha-beta and SSS∗ search procedures.T. Ibaraki - 1986 - Artificial Intelligence 29 (1):73-117.
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  • Multi-player alpha-beta pruning.Richard E. Korf - 1991 - Artificial Intelligence 48 (1):99-111.
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  • Time-efficient state space search.Alexander Reinefeld & Peter Ridinger - 1994 - Artificial Intelligence 71 (2):397-408.
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  • Best-first minimax search.Richard E. Korf & David Maxwell Chickering - 1996 - Artificial Intelligence 84 (1-2):299-337.
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  • Computer poker: A review.Jonathan Rubin & Ian Watson - 2011 - Artificial Intelligence 175 (5-6):958-987.
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  • The B∗ tree search algorithm—New results.Andrew J. Palay - 1982 - Artificial Intelligence 19 (2):145-163.
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  • Singular extensions.Thomas Anantharaman, Murray S. Campbell & Feng-Hsiung Hsu - 1990 - Artificial Intelligence 43 (1):99-109.
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  • A minimax algorithm better than alpha-beta?G. C. Stockman - 1979 - Artificial Intelligence 12 (2):179-196.
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  • Pattern knowledge and search:The SUPREM architecture.Hans Berliner & Carl Ebeling - 1989 - Artificial Intelligence 38 (2):161-198.
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  • A game tree with distinct leaf values which is easy for the alpha-beta algorithm.Ingo Althöfer & Bernhard Balkenhol - 1991 - Artificial Intelligence 52 (2):183-190.
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  • The last player theorem.Dana S. Nau - 1982 - Artificial Intelligence 18 (1):53-65.
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  • The multi-player version of minimax displays game-tree pathology.David Mutchler - 1993 - Artificial Intelligence 64 (2):323-336.
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  • Kazuyuki Tanaka's Work on AND-OR Trees and Subsequent Developments.Toshio Suzuki - 2017 - Annals of the Japan Association for Philosophy of Science 25:79-88.
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  • When is it better not to look ahead?Dana S. Nau, Mitja Luštrek, Austin Parker, Ivan Bratko & Matjaž Gams - 2010 - Artificial Intelligence 174 (16-17):1323-1338.
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  • Pathology on game trees revisited, and an alternative to minimaxing.Dana S. Nau - 1983 - Artificial Intelligence 21 (1-2):221-244.
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  • A generalised quiescence search algorithm.Don F. Beal - 1990 - Artificial Intelligence 43 (1):85-98.
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  • On the nature of pathology in game searching.Judea Pearl - 1983 - Artificial Intelligence 20 (4):427-453.
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  • Conspiracy numbers for min-max search.David Allen McAllester - 1988 - Artificial Intelligence 35 (3):287-310.
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  • Searching game trees under a partial order.Pallab Dasgupta, P. P. Chakrabarti & S. C. DeSarkar - 1996 - Artificial Intelligence 82 (1-2):237-257.
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  • Parallel Randomized Best-First Minimax Search.Yaron Shoham & Sivan Toledo - 2002 - Artificial Intelligence 137 (1-2):165-196.
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  • Using Game Description Language for mediated dispute resolution.Dave de Jonge, Tomas Trescak, Carles Sierra, Simeon Simoff & Ramon López de Mántaras - 2019 - AI and Society 34 (4):767-784.
    Mediation is a process in which two parties agree to resolve their dispute by negotiating over alternative solutions presented by a mediator. In order to construct such solutions, the mediator brings more information and knowledge, and, if possible, resources to the negotiation table. In order to do so, the mediator faces the challenge of determining which information is relevant to the current problem, given a vast database of knowledge. The contribution of this paper is the automated mediation machinery to resolve (...)
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  • On pruning search trees of impartial games.Piotr Beling & Marek Rogalski - 2020 - Artificial Intelligence 283 (C):103262.
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  • The efficiency of the alpha-beta search on trees with branch-dependent terminal node scores.M. M. Newborn - 1977 - Artificial Intelligence 8 (2):137-153.
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  • Reasoning with uncertainty in computer chess.Helmut Horacek - 1990 - Artificial Intelligence 43 (1):37-56.
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  • Computer Go: An AI oriented survey.Bruno Bouzy & Tristan Cazenave - 2001 - Artificial Intelligence 132 (1):39-103.
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  • Is real-valued minimax pathological?Mitja Luštrek, Matjaž Gams & Ivan Bratko - 2006 - Artificial Intelligence 170 (6-7):620-642.
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  • Benefits of using multivalued functions for minimaxing.Anton Scheucher & Hermann Kaindl - 1998 - Artificial Intelligence 99 (2):187-208.
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  • Partial order bounding: A new approach to evaluation in game tree search.Martin Müller - 2001 - Artificial Intelligence 129 (1-2):279-311.
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  • The PN∗-search algorithm: Application to tsume-shogi.Masahiro Seo, Hiroyuki Iida & Jos W. H. M. Uiterwijk - 2001 - Artificial Intelligence 129 (1-2):253-277.
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  • Equilibrium points of an AND–OR tree: Under constraints on probability.Toshio Suzuki & Yoshinao Niida - 2015 - Annals of Pure and Applied Logic 166 (11):1150-1164.
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  • Nagging: A scalable fault-tolerant paradigm for distributed search.Alberto Maria Segre, Sean Forman, Giovanni Resta & Andrew Wildenberg - 2002 - Artificial Intelligence 140 (1-2):71-106.
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  • A world championship caliber checkers program.Jonathan Schaeffer, Joseph Culberson, Norman Treloar, Brent Knight, Paul Lu & Duane Szafron - 1992 - Artificial Intelligence 53 (2-3):273-289.
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  • Game tree searching by min/max approximation.Ronald L. Rivest - 1987 - Artificial Intelligence 34 (1):77-96.
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  • Information capture and reuse strategies in Monte Carlo Tree Search, with applications to games of hidden information.Edward J. Powley, Peter I. Cowling & Daniel Whitehouse - 2014 - Artificial Intelligence 217 (C):92-116.
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  • General Branch and Bound, and its relation to A∗ and AO∗.Dana S. Nau, Vipin Kumar & Laveen Kanal - 1984 - Artificial Intelligence 23 (1):29-58.
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  • A general branch and bound formulation for understanding and synthesizing and/or tree search procedures.Vipin Kumar & Laveen N. Kanal - 1983 - Artificial Intelligence 21 (1-2):179-198.
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  • A quantitative analysis of the alpha-beta pruning algorithm.Nevin M. Darwish - 1983 - Artificial Intelligence 21 (4):405-433.
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  • Proof-number search.L. Victor Allis, Maarten van der Meulen & H. Jaap van den Herik - 1994 - Artificial Intelligence 66 (1):91-124.
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